Abstract:
Manual data inspection and seismogram interpretation requires processing for event detection, signal classification and data visualization. The use of machine learning techniques automates decision processes and reveals the statistical properties of data. This approach is becoming more and more important and valuable for large and complex seismic records. Unsupervised learning allows the recognition of features. Self-Organizing Maps (SOMs) are used for a data-driven feature selection, visualization and clustering of attributes. The aim of the project is to design an automatic method which clusters the attribute volumes of seismic images to segment different types of features using self-organizing maps.
